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A novel hypothesis-generating approach for detecting phenotypic associations using epigenetic data.

ABSTRACT: Aim: Hypotheses about what phenotypes to include in causal analyses, that in turn can have clinical and policy implications, can be guided by hypothesis-free approaches leveraging the epigenome, for example.Materials & methods: Minimally adjusted epigenome-wide association studies (EWAS) using ALSPAC data were performed for example conditions, dysmenorrhea and heavy menstrual bleeding (HMB). Differentially methylated CpGs were searched in the EWAS Catalog and associated traits identified. Traits were compared between those with and without the example conditions in ALSPAC.Results: Seven CpG sites were associated with dysmenorrhea and two with HMB. Smoking and adverse childhood experience score were associated with both conditions in the hypothesis-testing phase.Conclusion: Hypothesis-generating EWAS can help identify associations for future analyses.

SUBMITTER: Martin FZ

PROVIDER: S-EPMC11370959 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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A novel hypothesis-generating approach for detecting phenotypic associations using epigenetic data.

Martin Florence Z FZ Easey Kayleigh E KE Howe Laura D LD Fraser Abigail A Lawlor Deborah A DA Relton Caroline L CL Sharp Gemma C GC

Epigenomics 20240717 11-12

Aim: Hypotheses about what phenotypes to include in causal analyses, that in turn can have clinical and policy implications, can be guided by hypothesis-free approaches leveraging the epigenome, for example.Materials & methods: Minimally adjusted epigenome-wide association studies (EWAS) using ALSPAC data were performed for example conditions, dysmenorrhea and heavy menstrual bleeding (HMB). Differentially methylated CpGs were searched in the EWAS Catalog and associated traits iden ...[more]

PMID: 39016098

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